README.md

metadata

license: cc-by-4.0
language:
  - pt
pretty_name: PT-BR SciELO Articles (Brazilian Open-Access Research)
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
tags:
  - pt-br
  - brazilian-portuguese
  - academic
  - scientific
  - scielo
  - research
  - pretraining
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

PT-BR SciELO Articles (Brazilian Open-Access Research)

Part of the MagTina350m pretrain corpus release by Dataseek under the Magestic.ai brand. This is one of nine silver-layer datasets that fed dataseek/magtina350m-base.

Summary

154 K full-text Brazilian Portuguese research and review articles from SciELO Brazil (post-2010), spanning health sciences, social sciences, humanities and engineering. Avg ~34 KB per article — the largest academic-prose corpus in the MagTina350m mix.

Source and collection method

Source: SciELO articlemeta API → fulltext HTML scrape → HTML→text → PT-only gate → year ≥ 2010 → doctype ∈ {research-article, review-article}.

ETL script (in the MagTina1B repository): scripts/etl/24_scielo_articles_v1.py (public release of the ETL scripts is on the roadmap; until then the data card below documents the recipe in full).

Filters and deduplication

The following filters were applied before this dataset reached its silver (release-ready) state:

• doctype ∈ {research-article, review-article}
• year ≥ 2010
• lang = pt
• len(text) ≥ 500 chars

Global URL-normalised deduplication was applied across all web-derived corpora (webpages, news, blogs) so the same article does not appear twice across those three datasets.

Schema

Column	Type	Description
text	string	Article fulltext.
source	string	Always 'scielo.br'.
lang	string	Language code (typically 'pt').
year	int32	Publication year.
doctype	string	research-article
doc_id	string	SciELO PID (links back to article).
n_chars	int64	Character count.

Columns dropped at export (kept private as ETL internals): none

Size statistics

Metric	Value
Rows	154.2 K (154,218)
Characters	5.29 B (5,291,892,295)
Estimated tokens (PT-BR, chars / 4.5)	1.18 B
Compressed Parquet on disk	~2.96 GB

Used in MagTina350m pretrain: 1.176 B tokens (6.8 % of MagTina350m's 17.39 B-token pretrain budget).

How to load

from datasets import load_dataset

ds = load_dataset("dataseek/ptbr-scielo", split="train", streaming=True)
for row in ds.take(5):
    print(row["text"][:200])

Streaming is recommended for the larger configs. For the smaller datasets (ptbr-dou, ptbr-books-publicos) eager loading is fine.

Licensing

CC-BY 4.0 — the dominant license across SciELO Brazil (open access). Individual articles may carry CC-BY-NC variants; downstream users should honour the per-article licenses available via the SciELO API. Attribution by article DOI is required for redistribution.

Upstream attribution: SciELO Brazil — https://scielo.br/

Citation

If you use this dataset, please cite both the upstream source and MagTina350m:

@misc{magtina350m_pretrain_2026,
  title  = {MagTina350m pretrain corpus — PT-BR SciELO Articles (Brazilian Open-Access Research)},
  author = {Frasson, Ricardo and {Dataseek Team}},
  year   = 2026,
  publisher = {Hugging Face},
  url    = {https://huggingface.co/datasets/dataseek/ptbr-scielo}
}

Please also honour the upstream license terms — for CC-BY-derived data, attribution to the upstream creators is mandatory; for CC-BY-SA, downstream derivatives must remain CC-BY-SA-compatible.

Intended use

• Pre-training, continued pre-training, or domain-adapting of Brazilian Portuguese language models.
• PT-BR NLP research where statistically representative public-web / academic / legal / encyclopedic data is needed.
• Reproducing or improving on the MagTina350m result.

Known limitations and PII statement

• Text was NOT PII-scrubbed. URLs, emails, phone numbers and personal names that occurred in the source data may still be present. We strip zero-width characters and normalise Unicode but we do not run an NER pass.
• Crawled data carries upstream biases of CommonCrawl, Wikipedia, news outlets and academic institutions present in the source. We have not audited these.
• No safety filtering beyond langid and basic alpha-ratio gates. Hate-speech, spam and adult content present in the source remain unless caught incidentally.
• Provenance preserved at row level. Every row has either a url, source or doc_id column that points back to upstream — this is intentional, so consumers can re-license, redact or filter.

Related releases

• Model: dataseek/magtina350m-base (354.6 M params, pretrained on this corpus + 8 sibling datasets)
• Instruct model: dataseek/magtina350m-instruct
• Sibling datasets: see dataseek/ptbr-* for all nine corpora

License

cc-by-4.0